brain - npm Package Compare versions

Comparing version 0.6.0 to 0.6.1

lib/cross-validate.js

@@ -22,4 +22,6 @@ var _ = require("underscore")._;
     learningRate: classifier.learningRate,
-    hidden: classifier.hiddenSizes
+    hidden: classifier.hiddenSizes,
+    network: classifier.toJSON()
   });
+
   return stats;
@@ -41,8 +43,13 @@ }
     iterations: 0,
-    trainError: 0,
-    precision: 0,
-    accuracy: 0,
-    recall: 0
+    trainError: 0
   };
 
+  var stats = {
+    truePos: 0,
+    trueNeg: 0,
+    falsePos: 0,
+    falseNeg: 0,
+    total: 0
+  };
+
   var misclasses = [];
@@ -57,7 +64,13 @@
 
-    _(avgs).each(function(sum, i) {
-      avgs[i] = sum + result[i];
+    _(avgs).each(function(sum, stat) {
+      avgs[stat] = sum + result[stat];
     });
 
+    _(stats).each(function(sum, stat) {
+      stats[stat] = sum + result[stat];
+    })
+
     misclasses.push(result.misclasses);
+
+    return result;
   });
@@ -69,9 +82,15 @@
 
-  avgs.testSize = size;
-  avgs.trainSize = data.length - size;
+  stats.precision = stats.truePos / (stats.truePos + stats.falsePos);
+  stats.recall = stats.truePos / (stats.truePos + stats.falseNeg);
+  stats.accuracy = (stats.trueNeg + stats.truePos) / stats.total;
 
+  stats.testSize = size;
+  stats.trainSize = data.length - size;
+
   return {
     avgs: avgs,
+    stats: stats,
+    sets: results,
     misclasses: _(misclasses).flatten()
   };
 }

lib/neuralnetwork.js

@@ -364,17 +364,20 @@ var _ = require("underscore"),
     // return standalone function that mimics run()
-    return new Function("inputs",
+    return new Function("input",
 '  var net = ' + JSON.stringify(json) + ';\n\n\
-  for(var i = 1; i < net.layers.length; i++) {\n\
+  for (var i = 1; i < net.layers.length; i++) {\n\
     var layer = net.layers[i];\n\
-    var outputs = {};\n\
-    for(var id in layer) {\n\
+    var output = {};\n\
+    \n\
+    for (var id in layer) {\n\
       var node = layer[id];\n\
       var sum = node.bias;\n\
-      for(var iid in node.weights)\n\
-        sum += node.weights[iid] * inputs[iid];\n\
-      outputs[id] = (1/(1 + Math.exp(-sum)));\n\
+      \n\
+      for (var iid in node.weights) {\n\
+        sum += node.weights[iid] * input[iid];\n\
+      }\n\
+      output[id] = (1 / (1 + Math.exp(-sum)));\n\
     }\n\
-    inputs = outputs;\n\
+    input = output;\n\
   }\n\
-  return outputs;');
+  return output;');
   }
@@ -381,0 +384,0 @@ }

package.json

 {
     "name": "brain",
     "description": "Neural network library",
-    "version": "0.6.0",
+    "version": "0.6.1",
     "author": "Heather Arthur <[e-mail]>",
@@ -6,0 +6,0 @@ "repository": {

README.md

@@ -16,3 +16,3 @@ # brain
 There's no reason to use a neural network to figure out XOR however (-: so here's a more involved, realistic example:
-[Demo: training a neural network to recognize color contrast](http://harthur.github.com/brain/examples/blackorwhite.html)
+[Demo: training a neural network to recognize color contrast](http://harthur.github.com/brain/)
 
@@ -31,3 +31,3 @@ # Using in node
 #### Data format
-Each training pattern should have an `input` and an `output`, both of which can be either an array of numbers from `0` to `1` or a hash of numbers from `0` to `1`. For the [color constrast demo](http://harthur.github.com/brain/examples/blackorwhite.html) it looks something like this:
+Each training pattern should have an `input` and an `output`, both of which can be either an array of numbers from `0` to `1` or a hash of numbers from `0` to `1`. For the [color constrast demo](http://harthur.github.com/brain/) it looks something like this:
 
@@ -34,0 +34,0 @@ ```javascript

New alerts

Uses eval

Supply chain risk

Package uses dynamic code execution (e.g., eval()), which is a dangerous practice. This can prevent the code from running in certain environments and increases the risk that the code may contain exploits or malicious behavior.

Found 1 instance in 1 package

Fixed alerts

Major refactor

Supply chain risk

Package has recently undergone a major refactor. It may be unstable or indicate significant internal changes. Use caution when updating to versions that include significant changes.

Found 1 instance in 1 package

Improved metrics

Lines of code

3552

0.71%

Number of medium supply chain risk alerts

5

-16.67%

Worsened metrics

Total package byte prevSize

158761

-13.11%

Number of package files

24

-11.11%

No dependency changes